Learning from the uncertain:
Modelling and forecasting
infectious disease dynamics
Sebastian Funk
10 January, 2017
Wellcome Trust Centre for Human Genetics, Oxford
Summer 2014
\(y=ax+b\)
\begin{eqnarray}
\dot{S}&=&-R_0 \gamma \frac{S}{N}I\\
\dot{I}&=&+R_0 \gamma \frac{S}{N}I - \gamma I\\
\dot{R}&=&+\gamma I
\end{eqnarray}
\(y=ax+b\)
A semi-mechanistic model for real-time forecasting
The unknown
- Community/hospital/funeral transmission
- Spatial dynamics
- Changes in behaviour
- Changes in reporting
- Interventions
- Seasonality
- etc
The known
- Average incubation period (~9 days)
- Average infectious period (~11 days)
- Case-fatality rate (~70%)
WHO Ebola response team (2014)
Transmission intensity as a stochastic process
\(d\log(R_0(t)) = \sigma dW_t\)
Dureau (2013)
Particle MCMC
- Method for filtering trajectories consistent with data
- Highly parallelisable
Andrieu (2010), Murray (2013)
Forecasting the Ebola epidemic
"We were losing ourselves in details […] all we needed to know is, are the number of cases rising, falling or levelling off?"
Hans Rosling
Assessing forecasts
Learning from the uncertain
Filtered trajectories tell us something about dynamics
Example: Ebola outbreak in Lofa Country, Liberia
An attempt to tease out factors that controlled Ebola
An attempt to tease out factors that controlled Ebola
Example: age of infection in childhood infections
Outlook
Forecasts are becoming part of outbreak response
Forecasting challenges
Forecasting methodology is underdeveloped
Need methods to select the best model and
combine all available data streams
(individual/spatial/genetic/media)
New tools
New tools
Bayesian inference with state-space models in R
Acknowledgements
Anton Camacho, Adam Kucharski, Roz Eggo, John Edmunds (LSHTM)
Bruce Reeder, Etienne Gignoux, Iza Ciglenecki, Amanda Tiffany (MSF)
James Hensman (Lancaster), Lawrence Murray (Uppsala)
Thank you!
http://sbfnk.github.io
@sbfnk